The varying-coefficient single-index model has two distinguishing features: partially linear varying-coefficient functions and a single-index structure. This paper proposes a nonparametric method based on smoothing splines for estimating varying-coefficient functions and an unknown link...

Penalized likelihood method can be used for hazard estimation with lifetime data that are right-censored, left-truncated, and possibly with covariates. In this article, we are concerned with more scalable computation of the method and with the derivation and assessment of certain interval...

Generalized cross-validation (GCV) is a popular method for choosing the smoothing parameter in generalized spline smoothing when there are independent errors with common unknown variance. When data points are replicated, one can choose the smoothing parameter by minimizing one of three...

A semiparametric method based on smoothing spline is proposed for the estimation of varying-coefficient partially linear models. A simple and efficient method is proposed, based on a partial spline technique with a lower-dimensional approximation to simultaneously estimate the...

The Weibull distribution is popularly used to model lifetime distributions in many areas of applied statistics. This paper employs a penalized likelihood method to estimate the shape parameter and an unknown regression function simultaneously in a nonparametric Weibull regression. Four methods...

Case-control data are often used in medical-related applications, and most studies have applied parametric logistic regression to analyze such data. In this study, we investigated a semiparametric model for the analysis of case-control data by relaxing the linearity assumption of risk factors by...

Penalized likelihood method is among the most effective tools for nonparametric multivariate function estimation. Recently, a generic computation-oriented asymptotic theory has been developed in the density estimation setting, and been extended to other settings such as conditional density...